Overview

Dataset statistics

Number of variables15
Number of observations48842
Missing cells0
Missing cells (%)0.0%
Duplicate rows49
Duplicate rows (%)0.1%
Total size in memory5.6 MiB
Average record size in memory120.0 B

Variable types

Numeric12
Categorical3

Alerts

Dataset has 49 (0.1%) duplicate rowsDuplicates
relationship is highly overall correlated with sexHigh correlation
sex is highly overall correlated with relationshipHigh correlation
race is highly imbalanced (65.8%)Imbalance
workclass has 2799 (5.7%) zerosZeros
education has 1389 (2.8%) zerosZeros
marital-status has 6633 (13.6%) zerosZeros
occupation has 2809 (5.8%) zerosZeros
relationship has 19716 (40.4%) zerosZeros
capital-gain has 44807 (91.7%) zerosZeros
capital-loss has 46560 (95.3%) zerosZeros
native-country has 857 (1.8%) zerosZeros

Reproduction

Analysis started2023-12-07 22:50:05.723454
Analysis finished2023-12-07 22:50:22.477694
Duration16.75 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct74
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.643585
Minimum17
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size381.7 KiB
2023-12-07T22:50:22.537576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile19
Q128
median37
Q348
95-th percentile63
Maximum90
Range73
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.71051
Coefficient of variation (CV)0.35479394
Kurtosis-0.18426874
Mean38.643585
Median Absolute Deviation (MAD)10
Skewness0.55758032
Sum1887430
Variance187.97808
MonotonicityNot monotonic
2023-12-07T22:50:22.652009image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 1348
 
2.8%
35 1337
 
2.7%
33 1335
 
2.7%
23 1329
 
2.7%
31 1325
 
2.7%
34 1303
 
2.7%
28 1280
 
2.6%
37 1280
 
2.6%
30 1278
 
2.6%
38 1264
 
2.6%
Other values (64) 35763
73.2%
ValueCountFrequency (%)
17 595
1.2%
18 862
1.8%
19 1053
2.2%
20 1113
2.3%
21 1096
2.2%
22 1178
2.4%
23 1329
2.7%
24 1206
2.5%
25 1195
2.4%
26 1153
2.4%
ValueCountFrequency (%)
90 55
0.1%
89 2
 
< 0.1%
88 6
 
< 0.1%
87 3
 
< 0.1%
86 1
 
< 0.1%
85 5
 
< 0.1%
84 13
 
< 0.1%
83 11
 
< 0.1%
82 15
 
< 0.1%
81 37
0.1%

workclass
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8704394
Minimum0
Maximum8
Zeros2799
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size381.7 KiB
2023-12-07T22:50:22.754817image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median4
Q34
95-th percentile6
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4642337
Coefficient of variation (CV)0.378312
Kurtosis1.6419714
Mean3.8704394
Median Absolute Deviation (MAD)0
Skewness-0.74790973
Sum189040
Variance2.1439803
MonotonicityNot monotonic
2023-12-07T22:50:22.837394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4 33906
69.4%
6 3862
 
7.9%
2 3136
 
6.4%
0 2799
 
5.7%
7 1981
 
4.1%
5 1695
 
3.5%
1 1432
 
2.9%
8 21
 
< 0.1%
3 10
 
< 0.1%
ValueCountFrequency (%)
0 2799
 
5.7%
1 1432
 
2.9%
2 3136
 
6.4%
3 10
 
< 0.1%
4 33906
69.4%
5 1695
 
3.5%
6 3862
 
7.9%
7 1981
 
4.1%
8 21
 
< 0.1%
ValueCountFrequency (%)
8 21
 
< 0.1%
7 1981
 
4.1%
6 3862
 
7.9%
5 1695
 
3.5%
4 33906
69.4%
3 10
 
< 0.1%
2 3136
 
6.4%
1 1432
 
2.9%
0 2799
 
5.7%

fnlwgt
Real number (ℝ)

Distinct28523
Distinct (%)58.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189664.13
Minimum12285
Maximum1490400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size381.7 KiB
2023-12-07T22:50:22.952449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum12285
5-th percentile39615.4
Q1117550.5
median178144.5
Q3237642
95-th percentile379481.65
Maximum1490400
Range1478115
Interquartile range (IQR)120091.5

Descriptive statistics

Standard deviation105604.03
Coefficient of variation (CV)0.55679491
Kurtosis6.0578482
Mean189664.13
Median Absolute Deviation (MAD)60295.5
Skewness1.4388919
Sum9.2635757 × 109
Variance1.115221 × 1010
MonotonicityNot monotonic
2023-12-07T22:50:23.071422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
203488 21
 
< 0.1%
120277 19
 
< 0.1%
190290 19
 
< 0.1%
125892 18
 
< 0.1%
126569 18
 
< 0.1%
126675 17
 
< 0.1%
113364 17
 
< 0.1%
99185 17
 
< 0.1%
186934 16
 
< 0.1%
111567 16
 
< 0.1%
Other values (28513) 48664
99.6%
ValueCountFrequency (%)
12285 1
 
< 0.1%
13492 1
 
< 0.1%
13769 3
< 0.1%
13862 1
 
< 0.1%
14878 1
 
< 0.1%
18827 1
 
< 0.1%
19214 1
 
< 0.1%
19302 6
< 0.1%
19395 2
 
< 0.1%
19410 2
 
< 0.1%
ValueCountFrequency (%)
1490400 1
< 0.1%
1484705 1
< 0.1%
1455435 1
< 0.1%
1366120 1
< 0.1%
1268339 1
< 0.1%
1226583 1
< 0.1%
1210504 1
< 0.1%
1184622 1
< 0.1%
1161363 1
< 0.1%
1125613 1
< 0.1%

education
Real number (ℝ)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.28842
Minimum0
Maximum15
Zeros1389
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size381.7 KiB
2023-12-07T22:50:23.174312image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median11
Q312
95-th percentile15
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.8744924
Coefficient of variation (CV)0.37658771
Kurtosis0.67657631
Mean10.28842
Median Absolute Deviation (MAD)2
Skewness-0.93629868
Sum502507
Variance15.011692
MonotonicityNot monotonic
2023-12-07T22:50:23.257679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
11 15784
32.3%
15 10878
22.3%
9 8025
16.4%
12 2657
 
5.4%
8 2061
 
4.2%
1 1812
 
3.7%
7 1601
 
3.3%
0 1389
 
2.8%
5 955
 
2.0%
14 834
 
1.7%
Other values (6) 2846
 
5.8%
ValueCountFrequency (%)
0 1389
 
2.8%
1 1812
 
3.7%
2 657
 
1.3%
3 247
 
0.5%
4 509
 
1.0%
5 955
 
2.0%
6 756
 
1.5%
7 1601
 
3.3%
8 2061
 
4.2%
9 8025
16.4%
ValueCountFrequency (%)
15 10878
22.3%
14 834
 
1.7%
13 83
 
0.2%
12 2657
 
5.4%
11 15784
32.3%
10 594
 
1.2%
9 8025
16.4%
8 2061
 
4.2%
7 1601
 
3.3%
6 756
 
1.5%

education-num
Real number (ℝ)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.078089
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size381.7 KiB
2023-12-07T22:50:23.349172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q19
median10
Q312
95-th percentile14
Maximum16
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.5709728
Coefficient of variation (CV)0.2551052
Kurtosis0.62574527
Mean10.078089
Median Absolute Deviation (MAD)1
Skewness-0.31652486
Sum492234
Variance6.6099009
MonotonicityNot monotonic
2023-12-07T22:50:23.429979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
9 15784
32.3%
10 10878
22.3%
13 8025
16.4%
14 2657
 
5.4%
11 2061
 
4.2%
7 1812
 
3.7%
12 1601
 
3.3%
6 1389
 
2.8%
4 955
 
2.0%
15 834
 
1.7%
Other values (6) 2846
 
5.8%
ValueCountFrequency (%)
1 83
 
0.2%
2 247
 
0.5%
3 509
 
1.0%
4 955
 
2.0%
5 756
 
1.5%
6 1389
 
2.8%
7 1812
 
3.7%
8 657
 
1.3%
9 15784
32.3%
10 10878
22.3%
ValueCountFrequency (%)
16 594
 
1.2%
15 834
 
1.7%
14 2657
 
5.4%
13 8025
16.4%
12 1601
 
3.3%
11 2061
 
4.2%
10 10878
22.3%
9 15784
32.3%
8 657
 
1.3%
7 1812
 
3.7%

marital-status
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6187503
Minimum0
Maximum6
Zeros6633
Zeros (%)13.6%
Negative0
Negative (%)0.0%
Memory size381.7 KiB
2023-12-07T22:50:23.518596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median2
Q34
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5077026
Coefficient of variation (CV)0.57573361
Kurtosis-0.53619399
Mean2.6187503
Median Absolute Deviation (MAD)2
Skewness-0.01632824
Sum127905
Variance2.273167
MonotonicityNot monotonic
2023-12-07T22:50:23.589794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 22379
45.8%
4 16117
33.0%
0 6633
 
13.6%
5 1530
 
3.1%
6 1518
 
3.1%
3 628
 
1.3%
1 37
 
0.1%
ValueCountFrequency (%)
0 6633
 
13.6%
1 37
 
0.1%
2 22379
45.8%
3 628
 
1.3%
4 16117
33.0%
5 1530
 
3.1%
6 1518
 
3.1%
ValueCountFrequency (%)
6 1518
 
3.1%
5 1530
 
3.1%
4 16117
33.0%
3 628
 
1.3%
2 22379
45.8%
1 37
 
0.1%
0 6633
 
13.6%

occupation
Real number (ℝ)

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5776995
Minimum0
Maximum14
Zeros2809
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size381.7 KiB
2023-12-07T22:50:23.672447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7
Q310
95-th percentile13
Maximum14
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.2305094
Coefficient of variation (CV)0.64315942
Kurtosis-1.2362805
Mean6.5776995
Median Absolute Deviation (MAD)4
Skewness0.1105506
Sum321268
Variance17.89721
MonotonicityNot monotonic
2023-12-07T22:50:23.752103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
10 6172
12.6%
3 6112
12.5%
4 6086
12.5%
1 5611
11.5%
12 5504
11.3%
8 4923
10.1%
7 3022
6.2%
0 2809
5.8%
14 2355
 
4.8%
6 2072
 
4.2%
Other values (5) 4176
8.6%
ValueCountFrequency (%)
0 2809
5.8%
1 5611
11.5%
2 15
 
< 0.1%
3 6112
12.5%
4 6086
12.5%
5 1490
 
3.1%
6 2072
 
4.2%
7 3022
6.2%
8 4923
10.1%
9 242
 
0.5%
ValueCountFrequency (%)
14 2355
 
4.8%
13 1446
 
3.0%
12 5504
11.3%
11 983
 
2.0%
10 6172
12.6%
9 242
 
0.5%
8 4923
10.1%
7 3022
6.2%
6 2072
 
4.2%
5 1490
 
3.1%

relationship
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4432865
Minimum0
Maximum5
Zeros19716
Zeros (%)40.4%
Negative0
Negative (%)0.0%
Memory size381.7 KiB
2023-12-07T22:50:23.838124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6021512
Coefficient of variation (CV)1.1100715
Kurtosis-0.75411638
Mean1.4432865
Median Absolute Deviation (MAD)1
Skewness0.79171931
Sum70493
Variance2.5668885
MonotonicityNot monotonic
2023-12-07T22:50:23.915254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 19716
40.4%
1 12583
25.8%
3 7581
 
15.5%
4 5125
 
10.5%
5 2331
 
4.8%
2 1506
 
3.1%
ValueCountFrequency (%)
0 19716
40.4%
1 12583
25.8%
2 1506
 
3.1%
3 7581
 
15.5%
4 5125
 
10.5%
5 2331
 
4.8%
ValueCountFrequency (%)
5 2331
 
4.8%
4 5125
 
10.5%
3 7581
 
15.5%
2 1506
 
3.1%
1 12583
25.8%
0 19716
40.4%

race
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size381.7 KiB
4
41762 
2
4685 
1
 
1519
0
 
470
3
 
406

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters48842
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row2
5th row2

Common Values

ValueCountFrequency (%)
4 41762
85.5%
2 4685
 
9.6%
1 1519
 
3.1%
0 470
 
1.0%
3 406
 
0.8%

Length

2023-12-07T22:50:23.997403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-07T22:50:24.092062image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
4 41762
85.5%
2 4685
 
9.6%
1 1519
 
3.1%
0 470
 
1.0%
3 406
 
0.8%

Most occurring characters

ValueCountFrequency (%)
4 41762
85.5%
2 4685
 
9.6%
1 1519
 
3.1%
0 470
 
1.0%
3 406
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48842
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 41762
85.5%
2 4685
 
9.6%
1 1519
 
3.1%
0 470
 
1.0%
3 406
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 48842
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 41762
85.5%
2 4685
 
9.6%
1 1519
 
3.1%
0 470
 
1.0%
3 406
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 41762
85.5%
2 4685
 
9.6%
1 1519
 
3.1%
0 470
 
1.0%
3 406
 
0.8%

sex
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size381.7 KiB
1
32650 
0
16192 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters48842
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 32650
66.8%
0 16192
33.2%

Length

2023-12-07T22:50:24.170673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-07T22:50:24.255224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 32650
66.8%
0 16192
33.2%

Most occurring characters

ValueCountFrequency (%)
1 32650
66.8%
0 16192
33.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48842
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 32650
66.8%
0 16192
33.2%

Most occurring scripts

ValueCountFrequency (%)
Common 48842
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 32650
66.8%
0 16192
33.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 32650
66.8%
0 16192
33.2%

capital-gain
Real number (ℝ)

Distinct123
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1079.0676
Minimum0
Maximum99999
Zeros44807
Zeros (%)91.7%
Negative0
Negative (%)0.0%
Memory size381.7 KiB
2023-12-07T22:50:24.344693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5013
Maximum99999
Range99999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7452.0191
Coefficient of variation (CV)6.9059796
Kurtosis152.6931
Mean1079.0676
Median Absolute Deviation (MAD)0
Skewness11.894659
Sum52703821
Variance55532588
MonotonicityNot monotonic
2023-12-07T22:50:24.459792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 44807
91.7%
15024 513
 
1.1%
7688 410
 
0.8%
7298 364
 
0.7%
99999 244
 
0.5%
3103 152
 
0.3%
5178 146
 
0.3%
5013 117
 
0.2%
4386 108
 
0.2%
8614 82
 
0.2%
Other values (113) 1899
 
3.9%
ValueCountFrequency (%)
0 44807
91.7%
114 8
 
< 0.1%
401 5
 
< 0.1%
594 52
 
0.1%
914 10
 
< 0.1%
991 6
 
< 0.1%
1055 37
 
0.1%
1086 8
 
< 0.1%
1111 1
 
< 0.1%
1151 13
 
< 0.1%
ValueCountFrequency (%)
99999 244
0.5%
41310 3
 
< 0.1%
34095 6
 
< 0.1%
27828 58
 
0.1%
25236 14
 
< 0.1%
25124 6
 
< 0.1%
22040 1
 
< 0.1%
20051 49
 
0.1%
18481 2
 
< 0.1%
15831 8
 
< 0.1%

capital-loss
Real number (ℝ)

Distinct99
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.502314
Minimum0
Maximum4356
Zeros46560
Zeros (%)95.3%
Negative0
Negative (%)0.0%
Memory size381.7 KiB
2023-12-07T22:50:24.577951image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4356
Range4356
Interquartile range (IQR)0

Descriptive statistics

Standard deviation403.00455
Coefficient of variation (CV)4.6056445
Kurtosis20.014346
Mean87.502314
Median Absolute Deviation (MAD)0
Skewness4.5698089
Sum4273788
Variance162412.67
MonotonicityNot monotonic
2023-12-07T22:50:24.687406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 46560
95.3%
1902 304
 
0.6%
1977 253
 
0.5%
1887 233
 
0.5%
2415 72
 
0.1%
1485 71
 
0.1%
1848 67
 
0.1%
1590 62
 
0.1%
1602 62
 
0.1%
1876 59
 
0.1%
Other values (89) 1099
 
2.3%
ValueCountFrequency (%)
0 46560
95.3%
155 1
 
< 0.1%
213 5
 
< 0.1%
323 5
 
< 0.1%
419 3
 
< 0.1%
625 17
 
< 0.1%
653 4
 
< 0.1%
810 2
 
< 0.1%
880 6
 
< 0.1%
974 2
 
< 0.1%
ValueCountFrequency (%)
4356 3
 
< 0.1%
3900 2
 
< 0.1%
3770 4
 
< 0.1%
3683 2
 
< 0.1%
3175 2
 
< 0.1%
3004 5
 
< 0.1%
2824 14
< 0.1%
2754 2
 
< 0.1%
2603 7
< 0.1%
2559 17
< 0.1%

hours-per-week
Real number (ℝ)

Distinct96
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.422382
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size381.7 KiB
2023-12-07T22:50:24.801617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17.05
Q140
median40
Q345
95-th percentile60
Maximum99
Range98
Interquartile range (IQR)5

Descriptive statistics

Standard deviation12.391444
Coefficient of variation (CV)0.30654908
Kurtosis2.9510591
Mean40.422382
Median Absolute Deviation (MAD)3
Skewness0.23874966
Sum1974310
Variance153.54789
MonotonicityNot monotonic
2023-12-07T22:50:24.917261image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 22803
46.7%
50 4246
 
8.7%
45 2717
 
5.6%
60 2177
 
4.5%
35 1937
 
4.0%
20 1862
 
3.8%
30 1700
 
3.5%
55 1051
 
2.2%
25 958
 
2.0%
48 770
 
1.6%
Other values (86) 8621
 
17.7%
ValueCountFrequency (%)
1 27
 
0.1%
2 53
 
0.1%
3 59
 
0.1%
4 84
 
0.2%
5 95
 
0.2%
6 92
 
0.2%
7 45
 
0.1%
8 218
0.4%
9 27
 
0.1%
10 425
0.9%
ValueCountFrequency (%)
99 137
0.3%
98 14
 
< 0.1%
97 2
 
< 0.1%
96 9
 
< 0.1%
95 2
 
< 0.1%
94 1
 
< 0.1%
92 3
 
< 0.1%
91 3
 
< 0.1%
90 42
 
0.1%
89 3
 
< 0.1%

native-country
Real number (ℝ)

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.749355
Minimum0
Maximum41
Zeros857
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size381.7 KiB
2023-12-07T22:50:25.031346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19
Q139
median39
Q339
95-th percentile39
Maximum41
Range41
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.7753432
Coefficient of variation (CV)0.21157768
Kurtosis12.772293
Mean36.749355
Median Absolute Deviation (MAD)0
Skewness-3.6895285
Sum1794912
Variance60.455961
MonotonicityNot monotonic
2023-12-07T22:50:25.335000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
39 43832
89.7%
26 951
 
1.9%
0 857
 
1.8%
30 295
 
0.6%
11 206
 
0.4%
33 184
 
0.4%
2 182
 
0.4%
8 155
 
0.3%
19 151
 
0.3%
5 138
 
0.3%
Other values (32) 1891
 
3.9%
ValueCountFrequency (%)
0 857
1.8%
1 28
 
0.1%
2 182
 
0.4%
3 122
 
0.2%
4 85
 
0.2%
5 138
 
0.3%
6 103
 
0.2%
7 45
 
0.1%
8 155
 
0.3%
9 127
 
0.3%
ValueCountFrequency (%)
41 23
 
< 0.1%
40 86
 
0.2%
39 43832
89.7%
38 27
 
0.1%
37 30
 
0.1%
36 65
 
0.1%
35 115
 
0.2%
34 21
 
< 0.1%
33 184
 
0.4%
32 67
 
0.1%

target
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size381.7 KiB
1
37155 
0
11687 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters48842
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 37155
76.1%
0 11687
 
23.9%

Length

2023-12-07T22:50:25.426378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-07T22:50:25.510047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 37155
76.1%
0 11687
 
23.9%

Most occurring characters

ValueCountFrequency (%)
1 37155
76.1%
0 11687
 
23.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48842
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 37155
76.1%
0 11687
 
23.9%

Most occurring scripts

ValueCountFrequency (%)
Common 48842
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 37155
76.1%
0 11687
 
23.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48842
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 37155
76.1%
0 11687
 
23.9%

Interactions

2023-12-07T22:50:20.931490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:07.619412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:08.821408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:10.040250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:11.248696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:12.441669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:13.799301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:14.970054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:16.162074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:17.311922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:18.464695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:19.781845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:21.030123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:07.725681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:08.927282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:10.144153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:11.352582image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:12.712426image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:13.901081image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:15.072920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:16.263474image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:17.412570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:18.562685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:19.880650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:21.129728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:07.832961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:09.033696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:10.252130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:11.457605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:12.818342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:14.004843image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:15.179773image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:16.365002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:17.515213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:18.663094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:19.982561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:21.230326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:07.938591image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:09.142731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:10.356113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:11.563301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:12.923222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:14.107668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:15.285917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:16.467018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:17.616637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:18.762190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:20.084150image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:21.326379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:08.042713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:09.246670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:10.460524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:11.664812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:13.024411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:14.208685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:15.388136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:16.564851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:17.715556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:18.858373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:20.182926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:21.425362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:08.145685image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:09.353796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:10.565862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:11.768434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:13.126508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:14.309835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:15.491051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:16.666077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:17.815607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:18.955640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:20.283627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:21.518549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:08.245720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:09.454946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:10.665988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:11.869106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:13.225328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:14.408055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:15.589598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:16.760472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:17.910952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:19.048251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:20.380015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:21.617099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:08.348203image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:09.559693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:10.770336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:11.970616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:13.326994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:14.509048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:15.691490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:16.859918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:18.010382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:19.144490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:20.479059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:21.706498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:08.444183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:09.658005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:10.867197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:12.066088image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:13.422063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:14.602584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:15.786315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:16.950328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:18.102436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:19.233694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:20.572194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:21.797856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:08.540513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:09.755115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:10.964910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:12.161380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:13.518913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:14.695841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:15.882988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:17.043614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:18.194531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:19.521001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:20.664787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:21.883156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:08.633454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:09.849046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:11.058033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:12.253526image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:13.610703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:14.785937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:15.974126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:17.131323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:18.283183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:19.605434image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:20.753725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:21.974287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:08.729066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:09.945875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:11.155713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:12.349289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:13.707613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:14.879303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:16.070405image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:17.224089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:18.376046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:19.695973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-12-07T22:50:20.844032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-12-07T22:50:25.585026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ageworkclassfnlwgteducationeducation-nummarital-statusoccupationrelationshipcapital-gaincapital-losshours-per-weeknative-countryracesextarget
age1.0000.069-0.078-0.0320.063-0.3730.002-0.3220.1240.0580.1470.0050.0270.1250.316
workclass0.0691.000-0.0290.0020.044-0.0740.211-0.1170.0300.0130.135-0.0070.0570.1510.181
fnlwgt-0.078-0.0291.000-0.014-0.0300.0360.0010.014-0.009-0.001-0.016-0.0750.0700.0280.010
education-0.0320.002-0.0141.0000.213-0.010-0.0340.0190.0070.0080.0110.0820.0570.0660.252
education-num0.0630.044-0.0300.2131.000-0.0650.116-0.0930.1190.0770.1640.0520.0670.0730.360
marital-status-0.373-0.0740.036-0.010-0.0651.000-0.0190.318-0.075-0.043-0.207-0.0290.0820.4590.448
occupation0.0020.2110.001-0.0340.116-0.0191.000-0.0760.0210.0190.088-0.0050.0710.3740.312
relationship-0.322-0.1170.0140.019-0.0930.318-0.0761.000-0.101-0.064-0.303-0.0100.0970.6460.454
capital-gain0.1240.030-0.0090.0070.119-0.0750.021-0.1011.000-0.0660.0920.0170.0130.0490.271
capital-loss0.0580.013-0.0010.0080.077-0.0430.019-0.064-0.0661.0000.0600.0090.0120.0640.197
hours-per-week0.1470.135-0.0160.0110.164-0.2070.088-0.3030.0920.0601.0000.0120.0580.2400.269
native-country0.005-0.007-0.0750.0820.052-0.029-0.005-0.0100.0170.0090.0121.0000.2670.0390.081
race0.0270.0570.0700.0570.0670.0820.0710.0970.0130.0120.0580.2671.0000.1140.099
sex0.1250.1510.0280.0660.0730.4590.3740.6460.0490.0640.2400.0390.1141.0000.215
target0.3160.1810.0100.2520.3600.4480.3120.4540.2710.1970.2690.0810.0990.2151.000

Missing values

2023-12-07T22:50:22.107606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-07T22:50:22.343527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ageworkclassfnlwgteducationeducation-nummarital-statusoccupationrelationshipracesexcapital-gaincapital-losshours-per-weeknative-countrytarget
039.0777516.0913.0411412174.00.040.0391
150.0683311.0913.0240410.00.013.0391
238.04215646.0119.0061410.00.040.0391
353.04234721.017.0260210.00.040.0391
428.04338409.0913.02105200.00.040.051
537.04284582.01214.0245400.00.040.0391
649.04160187.065.0381200.00.016.0231
752.06209642.0119.0240410.00.045.0390
831.0445781.01214.041014014084.00.050.0390
942.04159449.0913.0240415178.00.040.0390
ageworkclassfnlwgteducationeducation-nummarital-statusoccupationrelationshipracesexcapital-gaincapital-losshours-per-weeknative-countrytarget
4883261.0489686.0119.02120410.00.048.0391
4883331.04440129.0119.0230410.00.040.0391
4883425.04350977.0119.0483400.00.040.0391
4883548.02349230.01214.0081410.00.040.0391
4883633.04245211.0913.04103410.00.040.0391
4883739.04215419.0913.00101400.00.036.0391
4883864.00321403.0119.0602210.00.040.0391
4883938.04374983.0913.02100410.00.050.0391
4884044.0483891.0913.0013115455.00.040.0391
4884135.05182148.0913.0240410.00.060.0390

Duplicate rows

Most frequently occurring

ageworkclassfnlwgteducationeducation-nummarital-statusoccupationrelationshipracesexcapital-gaincapital-losshours-per-weeknative-countrytarget# duplicates
1221.04243368.0131.0451410.00.050.02613
2325.04195994.032.0491400.00.040.01313
2425.04308144.0913.0431410.00.040.02613
017.04153021.028.04123400.00.020.03912
118.05378036.028.0453410.00.010.03912
219.00167428.01510.0403410.00.040.03912
319.0497261.0119.0451410.00.040.03912
419.04130431.043.0451410.00.036.02612
519.04138153.01510.0413400.00.010.03912
619.04139466.01510.04123400.00.025.03912